Results 1 to 10 of about 72,713 (317)
Inductive Logic Programming in Databases: from Datalog to DL+log
, 2010 In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through ...
Lisi, Francesca A.
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Structural Resolution with Co-inductive Loop Detection [PDF]
, 2017 A way to combine co-SLD style loop detection with structural resolution was found and is introduced in this work, to extend structural resolution with co-induction.
Li, Yue
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MP-SPILDL: A Massively Parallel Inductive Logic Learner in Description Logic
IEEE AccessThis article presents MP-SPILDL, a massively parallel inductive logic learner in Description Logic (DL). MP-SPILDL is a scalable inductive Logic Programming (ILP) algorithm that exploits existing Big Data infrastructure to perform large-scale inductive ...
Eyad Algahtani
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Bootstrapping Knowledge Graphs From Images and Text
Frontiers in Neurorobotics, 2019 The problem of generating structured Knowledge Graphs (KGs) is difficult and open but relevant to a range of tasks related to decision making and information augmentation.
Jiayuan Mao +7 more
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A three-valued logic for Inductive Logic Programming [PDF]
, 1999 Inductive Logic Programming (ILP) is closely related to Logic Programming (LP) by the name. We extract the basic differences of ILP and LP by comparing both and give definitions of the basic assumptions of their paradigms, e.g. closed world assumption,
Bell, Siegfried, Weber, Steffo
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Logic Programming Applications: What Are the Abstractions and Implementations?
, 2018 This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their
Liu, Yanhong A.
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Semantic Probabilistic Inference of Predictions
Известия Иркутского государственного университета: Серия "Математика", 2017 Prediction is one of the most important concepts in science. Predictions obtained from probabilistic knowledge, are described by an inductive-statistical inference (I-S inference).
E. E. Vityaev
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Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
Applied SciencesCurrent imitation learning approaches, predominantly based on deep neural networks (DNNs), offer efficient mechanisms for learning driving policies from real-world datasets.
Iman Sharifi +2 more
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GenEth: a general ethical dilemma analyzer
Paladyn, 2018 We argue that ethically significant behavior of autonomous systems should be guided by explicit ethical principles determined through a consensus of ethicists. Such a consensus is likely to emerge in many areas in which intelligent autonomous systems are
Anderson Michael, Anderson Susan Leigh
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Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME
, 2018 We present a heuristic based algorithm to induce \textit{nonmonotonic} logic programs that will explain the behavior of XGBoost trained classifiers.
Gupta, Gopal, Shakerin, Farhad
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